scholarly journals Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

2019 ◽  
Author(s):  
Xingmin Aaron Zhang ◽  
Amy Yates ◽  
Nicole Vasilevsky ◽  
JP Gourdine ◽  
Leigh C. Carmody ◽  
...  
Keyword(s):  
Laboratory Test ◽  
Electronic Health Record ◽  
Laboratory Tests ◽  
Health Record ◽  
Phenotype Ontology ◽  
Test Results ◽  
Human Phenotype ◽  
Laboratory Test Results ◽  
Electronic Health ◽  
Deep Phenotyping

AbstractElectronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.One Sentence SummaryWe present an approach to semantically integrating LOINC-encoded laboratory data with the Human Phenotype Ontology and show that the integrated LOINC data can be used to identify biomarkers for asthma from electronic health record data.

scholarly journals How Do Experienced Physicians Access and Evaluate Laboratory Test Results for the Chronic Patient? A Qualitative Analysis

2018 ◽  
Vol 09 (02) ◽  
pp. 403-410 ◽  
Author(s):  
Torbjørn Torsvik ◽  
Børge Lillebo ◽  
Morten Hertzum
Keyword(s):  
Chronic Illness ◽  
Laboratory Test ◽  
Electronic Health Record ◽  
Contextual Information ◽  
Health Record ◽  
Test Results ◽  
Chronic Patients ◽  
Laboratory Test Results ◽  
Significant Change ◽  
Electronic Health

Background Electronic health records may present laboratory test results in a variety of ways. Little is known about how the usefulness of different visualizations of laboratory test results is influenced by the complex and varied process of clinical decision making. Objective The purpose of this study was to investigate how clinicians access and utilize laboratory test results when caring for patients with chronic illness. Methods We interviewed 10 attending physicians about how they access and assess laboratory tests when following up patients with chronic illness. The interviews were audio-recorded, transcribed verbatim, and analyzed qualitatively. Results Informants preferred different visualizations of laboratory test results, depending on what aspects of the data they were interested in. As chronic patients may have laboratory test results that are permanently outside standardized reference ranges, informants would often look for significant change, rather than exact values. What constituted significant change depended on contextual information (e.g., the results of other investigations, intercurrent diseases, and medical interventions) spread across multiple locations in the electronic health record. For chronic patients, the temporal relations between data could often be of special interest. Informants struggled with finding and synthesizing fragmented information into meaningful overviews. Conclusion The presentation of laboratory test results should account for the large variety of associated contextual information needed for clinical comprehension. Future research is needed to improve the integration of the different parts of the electronic health record.

scholarly journals Creation and Use of an Electronic Health Record Reporting Database to Improve a Laboratory Test Utilization Program

2018 ◽  
Vol 09 (03) ◽  
pp. 519-527 ◽  
Author(s):  
Danielle Kurant ◽  
Jason Baron ◽  
Genti Strazimiri ◽  
Kent Lewandrowski ◽  
Joseph Rudolf ◽  
...  
Keyword(s):  
Information System ◽  
Laboratory Test ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Management Program ◽  
Health Record ◽  
Laboratory Information System ◽  
Utilization Management ◽  
Laboratory Utilization ◽  
Electronic Health

Objectives Laboratory-based utilization management programs typically rely primarily on data derived from the laboratory information system to analyze testing volumes for trends and utilization concerns. We wished to examine the ability of an electronic health record (EHR) laboratory orders database to improve a laboratory utilization program. Methods We obtained a daily file from our EHR containing data related to laboratory test ordering. We then used an automated process to import this file into a database to facilitate self-service queries and analysis. Results The EHR laboratory orders database has proven to be an important addition to our utilization management program. We provide three representative examples of how the EHR laboratory orders database has been used to address common utilization issues. We demonstrate that analysis of EHR laboratory orders data has been able to provide unique insights that cannot be obtained by review of laboratory information system data alone. Further, we provide recommendations on key EHR data fields of importance to laboratory utilization efforts. Conclusion We demonstrate that an EHR laboratory orders database may be a useful tool in the monitoring and optimization of laboratory testing. We recommend that health care systems develop and maintain a database of EHR laboratory orders data and integrate this data with their laboratory utilization programs.

scholarly journals Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease

2019 ◽  
Vol 26 (12) ◽  
pp. 1437-1447 ◽  
Author(s):  
Lisa Bastarache ◽  
Jacob J Hughey ◽  
Jeffrey A Goldstein ◽  
Julie A Bastraache ◽  
Satya Das ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Risk Score ◽  
Population Level ◽  
International Classification Of Diseases ◽  
Mendelian Disease ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Human Phenotype ◽  
Mendelian Diseases ◽  
Electronic Health

Abstract Objective The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features. Materials and Methods PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records. Results PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis. Discussion Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences. Conclusions PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.

Reduction of Laboratory Utilization in the Intensive Care Unit

2016 ◽  
Vol 32 (8) ◽  
pp. 500-507 ◽  
Author(s):  
Samih Raad ◽  
Rachel Elliott ◽  
Evan Dickerson ◽  
Babar Khan ◽  
Khalil Diab
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Laboratory Test ◽  
Laboratory Tests ◽  
Routine Laboratory ◽  
Test Results ◽  
Staff Education ◽  
Arterial Blood ◽  
Laboratory Test Results ◽  
Order Sets

Objective: In our academic intensive care unit (ICU), there is excess ordering of routine laboratory tests. This is partially due to a lack of transparency of laboratory-processing costs and to the admission order plans that favor daily laboratory test orders. We hypothesized that a program that involves physician and staff education and alters the current ICU order sets will lead to a sustained decrease in routine laboratory test ordering. Design: Prospective cohort study. Setting: Academic closed medical ICU (MICU). Patients: All patients admitted to the MICU. Methods: We consistently educated residents, faculty, and staff about laboratory test costs. We removed the daily laboratory test option from the admission order sets and asked residents to order needed laboratory test results every day. We only allowed the G3+I-STAT (arterial blood gas only) cartridges in the MICU in hopes of decreasing duplicative laboratory test results. We added laboratory review to the daily rounding checklist. Measurement and Main Results: Total number of laboratory tests per patient-day decreased from 39.43 to an average of 26.74 ( P <.001) over a 9-month period. The number of iSTAT laboratory tests per patient-day decreased from 7.37 to an average of 1.16 ( P < .001) over the same time period. The number of iSTAT/central laboratory processing duplicative laboratory tests per patient-day decreased from 0.17 to an average of 0.01 ( P < .001). The percentage of patients who have daily laboratory test orders decreased from 100% to an average of 11.94% ( P <. 001). US$123 436 in direct savings and US$258 035 dollars in indirect savings could be achieved with these trends. Intensive care unit morbidity and mortality were not impacted. Conclusion: A simple technique of resident, nursing, and ancillary staff education, combined with alterations in order sets using electronic medical records, can lead to a sustained reduction in laboratory test utilization over time and to significant cost savings without affecting patient safety.

scholarly journals Graphical display of diagnostic test results in electronic health Records: a comparison of 8 systems

2015 ◽  
Vol 22 (4) ◽  
pp. 900-904 ◽  
Author(s):  
Dean F Sittig ◽  
Daniel R Murphy ◽  
Michael W Smith ◽  
Elise Russo ◽  
Adam Wright ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Laboratory Test ◽  
Clinical Laboratory ◽  
Negative Impact ◽  
Laboratory Data ◽  
Test Results ◽  
Health Records ◽  
Laboratory Test Results ◽  
Data Points ◽  
Electronic Health

Abstract Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x–y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

scholarly journals An ancillary genomics system to support the return of pharmacogenomic results

2019 ◽  
Vol 26 (4) ◽  
pp. 306-310 ◽  
Author(s):  
Luke V Rasmussen ◽  
Maureen E Smith ◽  
Federico Almaraz ◽  
Stephen D Persell ◽  
Laura J Rasmussen-Torvik ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Health Record ◽  
Test Results ◽  
Actionable Knowledge ◽  
Genomic Test ◽  
Electronic Health ◽  
To Receive

AbstractExisting approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support.

scholarly journals Information Overload and Missed Test Results in Electronic Health Record–Based Settings

2013 ◽  
Vol 173 (8) ◽  
pp. 702 ◽  
Author(s):  
Hardeep Singh ◽  
Christiane Spitzmueller ◽  
Nancy J. Petersen ◽  
Mona K. Sawhney ◽  
Dean F. Sittig
Keyword(s):  
Electronic Health Record ◽  
Information Overload ◽  
Health Record ◽  
Test Results ◽  
Electronic Health
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